Abstract
This paper describes development of a contactless, low cost vision-based system for displacement measurement of civil structures. Displacement measurements provide a valuable insight into the structural condition and service behaviour of bridges under live loading. Conventional displacement gauges or GPS based systems have limitations in terms of access to the infrastructure and accuracy. The system introduced in this paper provides a low cost durable alternative which is rapidly deployable in the field and does not require direct contact or access to the infrastructure or its vicinity. A commercial action camera was modified to facilitate the use of a telescopic lens and paired with the development of robust displacement identification algorithms based on pattern matching. Performance was evaluated first in a series of controlled laboratory tests and validated against displacement measurements obtained using a fibre optic displacement gauge. The efficiency of the system for field applications was then demonstrated by capturing the validated bridge response of two structures under live loading including the iconic peace bridge. Located in the City of Derry, Northern Ireland, the Peace Bridge is a 310 m curved self-anchored suspension pedestrian bridge structure. The vision-based results of the field experiment were confirmed against displacements calculated from measured accelerations during a dynamic assessment of the structure under crowd loading. In field applications the developed system can achieve a root mean square error (RMSE) of 0.03 mm against verified measurements.
Original language | English |
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Pages (from-to) | 343-358 |
Number of pages | 16 |
Journal | Mechanical Systems and Signal Processing |
Volume | 121 |
Early online date | 23 Nov 2018 |
DOIs | |
Publication status | Published - 15 Apr 2019 |
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Development of a time-synchronised multi-input computer vision system for structural monitioring utilising deep learning for vehicle identification
Lydon, D. (Author), Taylor, S. (Supervisor), Martinez del Rincon, J. (Supervisor) & Hester, D. (Supervisor), Jul 2020Student thesis: Doctoral Thesis › Doctor of Philosophy
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